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dc.contributor.author Adelabu, S
dc.contributor.author Mutanga, O
dc.contributor.author Adam, E
dc.contributor.author Cho, Moses A
dc.date.accessioned 2014-02-13T08:58:17Z
dc.date.available 2014-02-13T08:58:17Z
dc.date.issued 2013-11
dc.identifier.citation Adelabu, S., Mutanga, O., Adam, E. and Cho, M.A. 2013. Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image. Journal of Applied Remote Sensing, vol. 7(1), pp 1-14 en_US
dc.identifier.issn 1931-3195
dc.identifier.uri http://remotesensing.spiedigitallibrary.org/article.aspx?articleid=1782792
dc.identifier.uri http://hdl.handle.net/10204/7206
dc.description Copyright: 2013 SPIE (Society of Photo-optical Instrumentation Engineers). Published in Journal of Applied Remote Sensing, vol. 7(1). en_US
dc.description.abstract Classification of different tree species in semiarid areas can be challenging as a result of the change in leaf structure and orientation due to soil moisture constraints. Tree species mapping is, however, a key parameter for forest management in semiarid environments. In this study, we examined the suitability of 5-band RapidEye satellite data for the classification of five tree species in mopane woodland of Botswana using machine leaning algorithms with limited training samples. We performed classification using random forest (RF) and support vector machines (SVM) based on EnMap box. The overall accuracies for classifying the five tree species was 88.75 and 85% for both SVM and RF, respectively. We also demonstrated that the new red-edge band in the RapidEye sensor has the potential for classifying tree species in semiarid environments when integrated with other standard bands. Similarly, we observed that where there are limited training samples, SVM is preferred over RF. Finally, we demonstrated that the two accuracy measures of quantity and allocation disagreement are simpler and more helpful for the vast majority of remote sensing classification process than the kappa coefficient. Overall, high species classification can be achieved using strategically located RapidEye bands integrated with advanced processing algorithms. en_US
dc.language.iso en en_US
dc.publisher SPIE (Society of Photo-optical Instrumentation Engineers) en_US
dc.relation.ispartofseries Workflow;12112
dc.subject Random forests en_US
dc.subject Support vector machines en_US
dc.subject Tree species classification en_US
dc.subject Semiarid environments en_US
dc.subject Red-edge en_US
dc.title Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image en_US
dc.type Article en_US


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